A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies
Figure 2
Bayesian network and outline of the inference schedule for VBQTL.
(a) The Bayesian network for the model of gene expression variation used in VBQTL (see Methods). The full model combines genetic (green), known factor (blue) and hidden factor (red) models to explain the observed gene expression levels . The solid rectangles indicate that contained variables are duplicated for each gene probe (
), SNP (
) or factor (
) respectively. A similar rectangle for individuals (
) is omitted in this representation. The dashed rectangle indicates that the variable
switches the contained part of the graph on or off representing the existence or lack of an association. Nodes with thick outlines (
,
and
) are observed. (b)–(e) Update cycle of the known factors model introduced in Section Inference. The red outline highlights the parts of the model that change in a step, and the thick blue arrows illustrate the flow of information. Details of these updates are discussed in the text.